Most idiomatic way of writing and reading an array at the same time


I have one array that gets modified a few times per second with one goroutine. In another function, I read a random value from the array once every few seconds. What would be the most Go-like way of approaching this problem?

In Java, for example, I would lock the array first and then read from it. I’m not sure if I could use channels for this because I don’t want to wait until my other function reads before I can continue updating the array.

package main

import (

const (
    SliceLength = 32

var mySlice []int

func randInt(min int, max int) int {
    return min + rand.Intn(max-min)

func writeSlice() {
    for index, _ := range mySlice {
        mySlice[index] = rand.Intn(100)

func main() {
    mySlice = make([]int, SliceLength)

    // First time just to populate it.

    // Write to the slice.
    go func() {
        for {
            time.Sleep(time.Duration(randInt(10, 50)) * time.Millisecond)

    // Read from slice.
    for {
        time.Sleep(time.Duration(randInt(1, 5)) * time.Second)
        fmt.Println(mySlice[randInt(0, SliceLength)])


A mutex sync.RWMutex can work here. You simply share your slice between two goroutines, locking and releasing before/after each slice operation.

Whilst you’re exploring possibilities, you should also consider what the “shared-nothing” principle would mean. You would want to find a solution that does not use a mutex and in which each goroutine has private data only – shared-nothing.

Suppose you had a third goroutine. Think of it as a server. It holds the slice (or any other contended data structure as required). When your first goroutine wants to make a change, it sends a message on a channel to the server, saying “change x to y”. When your second goroutine wants to read the data, it might simply read a copy of the data off a channel coming from the server.

Inside the server, a select statement will choose between incoming update messages and outgoing read messages – bear in mind that the guards can be on either channel inputs or channel outputs, so this is easy to do.

...  declare and initialise the slice
for {
    select {
    case instruction = <-update:
        ...  apply the instruction to the slice
    case output<- slice:
        ...  sent the slice to the consumer

Notice that the server code is itself single-threaded, i.e. there is no shared-memory access to the slice, even though the behaviour overall is concurrent. This is a nice feature of the CSP approach: locally, there is never any need to worry about shared access to data. “Shared-nothing” means what it says.

Furthermore, you can fully reason about the possible states of such a piece of code locally, without needing global knowledge. This is a BIG benefit.

Answered By – Rick-777

Answer Checked By – Katrina (GoLangFix Volunteer)

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